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OpenNash Earnings

Get KPI history

get_kpis
Read-onlyIdempotent

Quarterly KPI history from SEC XBRL filings: revenue, gross_profit, operating_income, net_income, eps_diluted, operating_cash_flow, capex, free_cash_flow, rd_expense and margins. Newest first; limit is the number of quarters.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>"
    },
    "name": "get_kpis"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
metricNoOne metric name, or omit for all
tickerYesStock ticker, such as NVDA

Output Schema

NameRequiredDescriptionDefault
noteYes
tickerYes
quartersYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, closed-world, non-destructive, so the safety profile is settled. The description usefully adds context the annotations do not: the data provenance (SEC XBRL filings) and the result ordering ("Newest first"), which tells the agent how to interpret and paginate results. It stops short of stating failure behavior for unknown tickers or metrics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero filler, with the data source and metric scope front-loaded and the ordering/limit semantics trailing compactly. Every clause carries information an agent needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-shape explanation is unnecessary, and the annotations cover safety and idempotency. What remains for a three-parameter read tool, provenance, metric vocabulary, ordering, and the meaning of limit, is all present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67%, with ticker and metric documented in the schema but limit carrying no description there. The description fills that gap precisely ("limit is the number of quarters") and lists the valid metric vocabulary, which the schema does not enumerate despite having no enum constraint. This is meaningful added meaning beyond the structured fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource and scope: "Quarterly KPI history from SEC XBRL filings," then enumerates the exact metric set (revenue, gross_profit, ... margins). That enumeration alone distinguishes it from generic siblings like get_company or get_quarter, though it never explicitly contrasts itself with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no mention of alternatives such as get_quarter for a single period or the search tools, and no prerequisites (e.g., whether the ticker must be a US SEC filer). The only operational hints, ordering and limit semantics, are about parameter behavior rather than tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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